ManiAgent: An Agentic Framework for General Robotic Manipulation

• URL: http://arxiv.org/abs/2510.11660v2
• Date: Tue, 14 Oct 2025 03:03:05 GMT
• Title: ManiAgent: An Agentic Framework for General Robotic Manipulation
• Authors: Yi Yang, Kefan Gu, Yuqing Wen, Hebei Li, Yucheng Zhao, Tiancai Wang, Xudong Liu,
• Abstract summary: We introduce ManiAgent, an agentic architecture for general manipulation tasks.<n>Multiple agents involve inter-agent communication to perform environmental perception, sub-task decomposition and action generation.<n>ManiAgent achieves an 86.8% success rate on the SimplerEnv benchmark and 95.8% on real-world pick-and-place tasks.
• Score: 30.154478145473792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning is limited by data scarcity and model capacity. To address this, we introduce ManiAgent, an agentic architecture for general manipulation tasks that achieves end-to-end output from task descriptions and environmental inputs to robotic manipulation actions. In this framework, multiple agents involve inter-agent communication to perform environmental perception, sub-task decomposition and action generation, enabling efficient handling of complex manipulation scenarios. Evaluations show ManiAgent achieves an 86.8% success rate on the SimplerEnv benchmark and 95.8% on real-world pick-and-place tasks, enabling efficient data collection that yields VLA models with performance comparable to those trained on human-annotated datasets. The project webpage is available at https://yi-yang929.github.io/ManiAgent/.

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